System and method for automated forest inventory mapping

ABSTRACT

A system and method for automated forest inventory mapping. The method may include receiving an image depicting an overhead view of a wooded area, the image comprising a plurality of pixels; receiving a set of climate data for a geographic region in which the wooded area is located; receiving a point cloud of a digital surface model of the wooded area; concatenating data corresponding to the plurality of pixels of the image, the set of climate data, and the point cloud into a feature vector; executing a machine learning model using the feature vector to generate timber data for each of the plurality of pixels of the image; and generating an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image.

BACKGROUND

The forest industry needs technological advances to help with a varietyof challenges. The forest industry deals with ever-fluctuating markets,cross-border tariffs, uncertainty on the timing and level of demand forhousing starts, and the forecasted surge in demand for engineered woodproducts. Furthermore, every year sees a wildfire catastrophe in majorproducing countries, while pest, disease and other disturbance destroyvast areas. Salvage operations cost dearly and the threat to forestresource supply compounds operational issues.

Sustainable forest management is essential, yet it is difficult andexpensive to ensure forest biodiversity, facilitate healthy ecosystems,and maintain and conserve clean soil and water resources. Faced withthese complexities, conventional lumber, paper, pulp, and wood productsmanufacturers look for new ways of monitoring and managing their foreststo drive down costs and realize value.

Traditionally, foresters have relied on infrequent and geographicallylimited aerial surveys, backed up by boots-on-the-ground assessments tomanage stands and monitor the impact of disturbances. Fillingintelligence gaps in-between surveys is a challenge as is getting groundresources to the right place at the right time to maximize impact.

SUMMARY

Aspects of example embodiments of the present disclosure relategenerally to providing an improved forest inventory by capturing adistribution and intermixing of different tree species within a forestand estimating a total volume and biomass of available timber in forestareas. Advantageously, the improved forest inventory system models treecount, height, and parameters to characterize the forest using opticaldata, synthetic-aperture radar (SAR) data, topographical data, and otherdata. The system, method, apparatus, and computer-readable mediumdescribed herein provide a technical improvement to modeling forests.

In accordance with some embodiments of the present disclosure, a methodis disclosed. The method may include receiving, by one or moreprocessors, an image depicting an overhead view of a wooded area, theimage comprising a plurality of pixels; receiving, by the one or moreprocessors, a set of climate data for a geographic region in which thewooded area is located; receiving, by the one or more processors, apoint cloud of a digital surface model of the wooded area;concatenating, by the one or more processors, data corresponding to theplurality of pixels of the image, the set of climate data, and the pointcloud into a feature vector; executing, by the one or more processors, amachine learning model using the feature vector to generate timber datafor each of the plurality of pixels of the image; and generating, by theone or more processors, an interactive overlay from the timber data, theinteractive overlay comprising the generated timber data for each of theplurality of pixels of the image.

In accordance with some other embodiments of the present disclosure, asystem is disclosed. The system may include one or more processorsconfigured by machine-readable instructions to receive an imagedepicting an overhead view of a wooded area, the image comprising aplurality of pixels; receive a set of climate data for a geographicregion in which the wooded area is located; receive a point cloud of adigital surface model of the wooded area; concatenate data correspondingto the plurality of pixels of the image, the set of climate data, andthe point cloud into a feature vector; execute a machine learning modelusing the feature vector to generate timber data for each of theplurality of pixels of the image; and generate an interactive overlayfrom the timber data, the interactive overlay comprising the generatedtimber data for each of the plurality of pixels of the image.

In accordance with yet other embodiments of the present disclosure, anon-transitory computer-readable media having computer-executableinstructions embodied thereon is disclosed. The computer-executableinstructions when executed by a processor, cause the processor toperform a process including receiving an image depicting an overheadview of a wooded area, the image comprising a plurality of pixels;receiving a set of climate data for a geographic region in which thewooded area is located; receiving a point cloud of a digital surfacemodel of the wooded area; concatenating data corresponding to theplurality of pixels of the image, the set of climate data, and the pointcloud into a feature vector; executing a machine learning model usingthe feature vector to generate timber data for each of the plurality ofpixels of the image; and generating an interactive overlay from thetimber data, the interactive overlay comprising the generated timberdata for each of the plurality of pixels of the image.

The foregoing summary is illustrative only and is not intended to be inany way limiting. In addition to the illustrative aspects, embodiments,and features described above, further aspects, embodiments, and featureswill become apparent by reference to the following drawings and thedetailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects of the present disclosure are best understood from the followingdetailed description when read with the accompanying figures. It isnoted that, in accordance with the standard practice in the industry,various features are not drawn to scale. In fact, the dimensions of thevarious features may be arbitrarily increased or reduced for clarity ofdiscussion.

FIG. 1 illustrates a summary of workflow and processing levels in thedevelopment of data products ranging from Level 0 (Source Data) throughto Level 3 (Final Data Products), in accordance with some embodiments.

FIG. 2 is an illustration of an example forest inventory managementsystem, in accordance with some embodiments.

FIG. 3 is a photograph of an overhead view of a wooded area, inaccordance with some embodiments.

FIG. 4 is a sequence diagram of zooming in on a particular region of aphotograph, in accordance with some embodiments.

FIGS. 5A and 5B depict different light bands with which satellite imagesmay be collected and an image that is captured using one of the lightbands, in accordance with some embodiments.

FIG. 6 is a photograph of an overhead view of a wooded area annotatedwith plots from which tree measurements have been collected, inaccordance with some embodiments.

FIG. 7 is an example of a training data set for training a machinelearning model to generate timber data, in accordance with someembodiments.

FIG. 8 is an illustration of an example feature vector that can be usedas an input into a machine learning model to generate timber data, inaccordance with some embodiments.

FIG. 9 is an example method for forest inventory management, inaccordance with some embodiments.

The foregoing and other features of the present disclosure will becomeapparent from the following description and appended claims, taken inconjunction with the accompanying drawings. Understanding that thesedrawings depict only several embodiments in accordance with thedisclosure and are therefore, not to be considered limiting of itsscope, the disclosure will be described with additional specificity anddetail through use of the accompanying drawings.

DETAILED DESCRIPTION

The following disclosure provides many different embodiments, orexamples, for implementing different features of the provided subjectmatter. Specific examples of components and arrangements are describedbelow to simplify the present disclosure. These are, of course, merelyexamples and are not intended to be limiting. In addition, the presentdisclosure may repeat reference numerals and/or letters in the variousexamples. This repetition is for the purpose of simplicity and clarityand does not in itself dictate a relationship between the variousembodiments and/or configurations discussed. Further, in the followingdetailed description, reference is made to the accompanying drawings,which form a part hereof. In the drawings, similar symbols typicallyidentify similar components, unless context dictates otherwise. Theillustrative embodiments described in the detailed description,drawings, and claims are not meant to be limiting. Other embodiments maybe utilized, and other changes may be made, without departing from thespirit or scope of the subject matter presented here. It will be readilyunderstood that the aspects of the present disclosure, as generallydescribed herein, and illustrated in the figures, can be arranged,substituted, combined, and designed in a wide variety of differentconfigurations, all of which are explicitly contemplated and made partof this disclosure.

As previously mentioned, when attempting to determine and maintain anaccurate record of a forest inventory, computers often times are forcedto rely on infrequent and geographically limited aerial surveys (e.g.,images taken using aerial devices) as well as boots-on-the-groundassessments. Filling intelligence gaps in-between surveys is a challengeas is getting ground resources to the right place at the right time tomaximize impact. Such methods are often inaccurate because theresolution of the limited aerial surveys may be too low for the computerto accurately identify objects within the surveys, the tree trunks ofthe trees may be hidden from view of the aerial survey by the trees'leaves, and the measurements of the boots-on-the-ground assessments maybe too infrequent and too difficult to capture over larger areas.

In one example, when a company requests to determine a count of the treevolume within a given area, a computer not using the methods describedherein may identify an image of the area and attempt to use objectrecognition techniques on the image. The computer may determine thenumber of trees that are within the image using such techniques. Thecomputer may then attempt to determine the amount of timber (or lumber)that is in the area based on the number of trees, providing a roughestimate of the amount of timber that is present in the area that may beinaccurate for a number of reasons as described above.

Implementations of the systems and methods discussed herein overcomethese technical deficiencies because they provide an improved method fordetermining a forestry inventory using artificial intelligenceprocessing. A computer may train a machine learning model to use animage in addition to other data (e.g., synthetic-aperture radar (SAR)imagery, optical imagery, geospatial data, and digital surface modeldata) to output timber data (e.g., forest inventory data) such as thevolume of the timber depicted in the image as well as other forestinventory data such as tree species distribution data and treemensuration data. The input data may include satellite data,incorporating datasets derived from both radar and optical satellitesensors. Other geospatial data sources such as elevation data may alsobe integrated where it is available in a suitable format and resolution,with all data sources processed to a resolution grid for subsequentanalysis and data product outputs. Inclusion of digital surface modeldata in the input data alone improves the accuracy of the machinelearning model's predictions compared to other methods and machinelearning models by 15-20%. Accordingly, upon receiving a request forforest inventory data for a particular region, the computer may executethe trained machine learning model using an image of the region as wellas the other data including the digital surface model data to obtainoutput timber data and provision the output timber data to therequesting device.

Thus, the present disclosure describes the use of satellite imagery andartificial intelligence (AI) processing techniques to remotely provide aview of an entire forest inventory across vast geographic areas and toanalyze disturbance events that threaten its value. This solution helpsmanage inventory, carbon stock, fire damage, pest, and disease,brushing, and mill optimization.

Advantageously, the embodiments described herein track the full forestlifecycle across seasons, fusing satellite and multiple data feeds withadvanced AI. The embodiments provide frequent, accurate insights todynamically manage inventory, driving large-scale efficiencies and costsavings, boosting productivity and competitive advantage, and optimizingtimber value.

FIG. 1 summarizes workflow and processing levels 100 in the developmentof data products ranging from Level 0 (Source Data) through to Level 3(Final Data Products). Source data can include synthetic-aperture radar(SAR) imagery, optical imagery, geospatial data, digital surface models,and training data. SAR imagery can be obtained from ESA Sentinel-1 andALOS-2 PALSAR satellites. Optical multi-spectral imagery can be obtainedfrom an ESA Sentinel-2 satellite.

Among the additional geospatial data, the system extracts elevation,slope, and aspect from databases such as the United States GeologicalService (USGS) National Elevation Dataset and climate data(precipitation, temperature, and solar radiation) from ClimNA, which maybe specific to North America. Soil data may also be included in themodeling from databases such as the gNATSGO database. Digital surfacemodel data can be included in the list of predictors to further increasethe accuracy of the model output. These sources are used to generateinputs to a model. The inputs can be SAR indices, spectral indices, andvalues for topographic variables.

The model may generate species distribution (e.g., the distribution andintermixing of different tree species within a forest) and/or treemensuration (e.g., estimates of the total volume and/or biomass ofavailable timber in forest areas and additionally models of total treecount, height and/or the diameter at breast height (DBH) parameters)data.

Referring now to FIG. 2 , an illustration of an example forest inventorymanagement system 200 is shown, in some embodiments. In brief overview,system 200 can include two client devices 202 and 204 that communicatewith a forest inventory manager 206 over a network 208. These componentsmay operate together to generate an overlay with timber data that canillustrate timber data about geographical regions represented byindividual pixels of images. System 200 may include more, fewer, ordifferent components than shown in FIG. 2 . For example, there may beany number of client devices or computers that make up or are a part offorest inventory manager 206 or networks in system 200.

Client devices 202 and 204 and/or forest inventory manager 206 caninclude or execute on one or more processors or computing devices and/orcommunicate via network 208. Network 208 can include computer networkssuch as the Internet, local, wide, metro, or other area networks,intranets, satellite networks, and other communication networks such asvoice or data mobile telephone networks. Network 208 can be used toaccess information resources such as web pages, websites, domain names,or uniform resource locators that can be presented, output, rendered, ordisplayed on at least one computing device (e.g., client device 202 or204), such as a laptop, desktop, tablet, personal digital assistant,smartphone, portable computers, or speaker. For example, via network208, client devices 202 and 204 can request, from forest inventorymanager 206, timber data about different geographic regions that aredepicted in aerial images of the regions.

Each of client devices 202 and 204 and/or forest inventory manager 206can include or utilize at least one processing unit or other logicdevices such as a programmable logic array engine or a module configuredto communicate with one another or other resources or databases. Thecomponents of client devices 202 and 204 and/or forest inventory manager206 can be separate components or a single component. System 200 and itscomponents can include hardware elements, such as one or moreprocessors, logic devices, or circuits.

Forest inventory manager 206 may comprise one or more processors thatare configured to generate timber data about geographic regions based onoptical data, SAR imagery, geospatial data, and digital surface modeldata. Forest inventory manager 206 may comprise a network interface 210,a processor 212, and/or memory 214. Forest inventory manager 206 maycommunicate with client devices 202 and 204 via network interface 210.Processor 212 may be or include an ASIC, one or more FPGAs, a DSP,circuits containing one or more processing components, circuitry forsupporting a microprocessor, a group of processing components, or othersuitable electronic processing components. In some embodiments,processor 212 may execute computer code or modules (e.g., executablecode, object code, source code, script code, machine code, etc.) storedin memory 214 to facilitate the activities described herein. Memory 214may be any volatile or non-volatile computer-readable storage mediumcapable of storing data or computer code.

Memory 214 may include a data collector 216, a data pre-processor 218, afeature vector generator 220, a machine learning model 222, a modeltrainer 224, a data post-processor 226, an overlay generator 228, and anormalization database 230. In brief overview, components 216-230 maycooperate to collect different types of data and images of ageographical region. Components 216-230 may generate a feature vectorfrom data and the images and input the feature vector into a machinelearning model that has been trained to output timber data forindividual pixels of images. The machine learning model may outputtimber data for the image and components 216-230 may generate aninteractive overlay from the timber data for display on a graphical userinterface (GUI) 232. Components 216-230 may place the interactiveoverlay over the image such that a user may select or place a cursorover the different pixels of the image on the GUI 232 to view timberdata for the geographic area that the image is depicting.

Data collector 216 may comprise programmable instructions that, uponexecution, cause processor 212 to collect geographical data fromdifferent sources. For example, data collector 216 may receive an imageof a wooded area. The image may be an optical photograph of the woodedarea taken from above the wooded area such as by a satellite or anotherflying vehicle. Data collector 216 may receive the image of the woodedarea from an entity or company that specializes in capturing andtransmitting such photographs. For example, data collector 216 mayreceive the image from an ESA Sentinel-2 satellite. Additionally, insome embodiments, data collector 216 may receive photographs or radardata of the wooded area such as photographs or radar data collected fromESA Sentinel-1 and/or ALOS-2 PALSAR satellites.

Data collector 216 may receive climate data for a geographic region ofthe wooded area. The geographic region may be the geographic area and/orcoordinates of the wooded area (e.g., the climate data for thecoordinates of the geographic area). The climate data may includeinformation about the climate of the wooded area (e.g., precipitation,temperature, solar radiation, etc.). Data collector 216 may receive theclimate data from an online database or from a data source provider thatcollects and maintains records of the climates around the world (e.g.,weather service providers, ClimNA, etc.). In some embodiments, datacollector 216 may receive other data related to the wooded area such asthe elevation and slope at different points within the wooded area or ofthe wooded area as a whole. Data collector 216 may receive such datafrom online data source providers such as, but not limited to, the USGSNational Elevation Dataset. In some embodiments, data collector 216 maycollect or receive soil data (e.g., the types of soil, the amount ofsoil, the PH level of the soil, etc.) about the wooded area. Datacollector 216 may receive such soil data from data source providers suchas, but not limited to, the gNATSGO database.

Data collector 216 may receive point cloud data of a digital surfacemodel for the wooded area. The point cloud data may include manydifferent metrics of the wooded area at various points. For example, thepoint cloud data may indicate the maximum height of the wooded area tobe the highest points on trees, buildings, hills, etc., within thewooded area. The point cloud data may have a 25 cm resolution (e.g., thepoint cloud may indicate the maximum height every 25 cm within thewooded area), or a resolution that is sharper than the resolution of theclimate data and/or optical or radar data. Data collector 216 mayreceive the point cloud data from a data source provider that providesdigital surface models for various geographic regions.

Data collector 216 may determine if the image, the climate data, and/orthe point cloud of the digital surface model have matching resolutions.For example, because the different types of data are collected fromdifferent sources and generally collected using different methods, thedata may be collected with different granularities and with differentlevels of detail. For example, the climate data and/or soil data may begeneric across the wooded area because there may not be much of adifference in climate or soil between the areas represented by thepixels of the image. However, other data, such as point cloud data,elevation data, and/or slope data, may have a higher resolution than thepixels of the image as it may be captured using a more nuanced device.Data collector 216 may compare the resolutions of the different types ofdata, including the image, data collector 216 has collected about thewooded area to determine if the data and the image have matchingresolutions.

Data pre-processor 218 may comprise programmable instructions that, uponexecution, cause processor 212 to pre-process the data that datacollector 216 collects into data with matching resolutions. For example,data pre-processor 218 may adjust the resolutions of the data inresponse to determining the data does not have a matching resolution.Data pre-processor 218 may determine the point cloud data of the digitalsurface model for the geographic region of the wooded area has aresolution of 25 centimeters and the resolution of the image is 10meters. Accordingly, data pre-processor 218 may reduce the resolution ofthe point cloud data to match the resolution of the image. To do so,data pre-processor 218 may identify the values (e.g., the height values)of the point cloud data that correspond to individual pixels of theimage (e.g., correspond to the same geographic area as the individualpixels of the image). Data pre-processor 218 may determine the averageheight values of the identified values for each pixel of the image andgenerate a vector from the average height values with a number ofdimensions that match the number of pixels of the image (e.g., a vectorwith an average height value for each pixel of the image). Similarly, inanother example, data pre-processor 218 may normalize the climate data,elevation data, slope data, and/or soil data into a vector that matchesthe number of pixels of the image. Such a vector may include the samevalue at each index value of the image unless more fine-grained data(e.g., higher resolution data) about the soil, climate, or elevation isavailable for the geographic area that is depicted in the image. Thispre-processing technique may enable data pre-processor 218 to evaluatethe image and determine timber data for the image on a pixel-by-pixelbasis.

Feature vector generator 220 may comprise programmable instructionsthat, upon execution, cause processor 212 to generate a feature vectorfrom the collected or received data. For example, feature vectorgenerator 220 may concatenate a feature vector from the received data.Feature vector generator 220 may do so in response to determining thecollected data has a matching resolution to the image and/or after datapre-processor 218 pre-processes the received data. Feature vectorgenerator 220 may concatenate the values of the point cloud (e.g., theadjusted values of the point cloud) and the climate data to the imagevector to create a feature vector that can be input into a machinelearning model. In some embodiments, feature vector generator 220 mayadditionally or instead concatenate soil data, radar data, elevationdata, etc., about the geographic region with the image vector to createthe feature vector. Accordingly, feature vector generator 220 maygenerate a feature vector using the image and information about thegeographic region depicted in the image that can be input into a machinelearning model to generate timber data about the vegetation of thewooded area depicted in the image.

In some embodiments, the feature vector may be a spreadsheet or may begenerated from a spreadsheet. For example, the feature vector may be aspreadsheet with each row representing data for each pixel of the image.The columns of the spreadsheet may represent the different values fromthe different data sources. For instance, for one pixel, a row mayinclude the numerical value representing the pixel from the image andone or more of the average heights of the point cloud data for thepixel, soil data for the pixel, elevation data for the pixel, radar datafor the pixel, slope data for the pixel, etc. Each row of thespreadsheet may have similar data for the individual pixels. In suchembodiments, concatenating the different types of data into a featurevector may including adding the values for the data into thespreadsheet. Feature vector generator 220 may input the spreadsheet intomachine learning model 222 as described herein.

In some embodiments, to input a spreadsheet into machine learning model222, feature vector generator 220 may retrieve the values for thedifferent types of data in the spreadsheet (e.g., values from thedifferent rows) and concatenate the values into a feature vector. Forexample, feature vector generator 220 may collect the data fromdifferent sources and organize the data into different columns of aspreadsheet. Feature vector generator 220 may execute a program thatretrieves values from the different columns column-by-column andconcatenates the values into a single feature vector. Thus, featurevector generator 220 may generate a feature vector from a spreadsheetcontaining the different types of data about the geographical datadepicted in an image.

Machine learning model 222 may comprise programmable instructions that,upon execution, cause processor 212 to output timber data (e.g., treespecies and tree mensuration data) for individual pixels of an imagebased on feature vectors containing the image and data about ageographical location depicted in the image. Machine learning model 22may contain or comprise one or more machine learning models (e.g.,support vector machines, neural networks, random forests, regressionalgorithms such as a gradient boosting algorithm, etc.) that can predictindividual types of timber data. Machine learning model 222 may beconfigured to receive feature vectors that are generated by featurevector generator 220 and determine output timber data using learnedparameters and/or weights of machine learning model 222. The timber datamay include forest species and/or forest mensuration data for individualpixels of the image. For example, feature vector generator 220 mayexecute machine learning model 222 using a feature vector for an imageof a geographic area and machine learning model 222 may outputpredictions of the distribution and intermixing of different tree and/orplant species at the geographic locations that are depicted by differentpixels of the image. In some embodiments, machine learning model 222 mayinstead or additionally output predictions for the total volume (e.g.,amount of timber in the trees), tree count, height, and/or DBHparameters at the geographic locations that are depicted by the pixelsof the image.

Model trainer 224 may comprise programmable instructions that, uponexecution, cause processor 212 to train machine learning model 222 topredict timber data for various images using training data setscomprising images of geographical areas, information about thegeographical areas, and a set of measurements of trees of thegeographical areas. Data collector 216 may receive sets of measurementsfor different areas of wooded areas depicted in images. The set ofmeasurements may be “cruise data” that is generated when techniciansventure into the wooded area (e.g., the forest) depicted in the imageand measure the vegetation (e.g., trees) in a series of discretelocations (e.g., plots). A plot may be a circular or other shaped areaand may be any size. In one example, plots may have any size and anyradius. The technicians may measure all or substantially all of thetrees in the plots. In doing so, the technicians may take measurementssuch as the DBH, height, and/or species of the individual trees withinthe plots. The technicians may submit the measured data to forestinventory manager 206 or another processing entity to send to forestinventory manager 206 as ground truth data about the vegetation of therespective plots. In some embodiments, the technicians may also countand transmit a total tree count of the plots. Data collector 216 maystore the sets of measurements in a database (not shown) within forestinventory manager 206 to be used as labels in training datasets.

In some embodiments, data collector 216 may receive the measured dataand use a set of equations (e.g., allometric equations) to determine thevolume and other information about the vegetation of the respectiveplots. For instance, data collector 216 may use allometric equations onthe measured data to determine the volume of the trees that weremeasured within the plots. Data collector 216 may also use the measureddata to determine the average and/or maximum height and/or DBH of thetrees within the plots (e.g., determine the average height or DBH basedon the measurements from the measured trees and/or identify the maximumheight and/or DBH based on the highest measurements). Accordingly, fromthe set of measurements that the technicians measure and transmit toforest inventory manager 206, data collector 216 may determine thevolume, average height, average DBH, maximum height, maximum DBH,species, and/or total tree count of individual plots within a geographicarea as the ground truth data for the plot.

Model trainer 224 may correlate the set of measurements with the pixelsof the image. To do so, model trainer 224 may identify the pixel or setof pixels of the image that correspond to the plots from which the setof measurements were taken. Model trainer 224 may identify rows of thespreadsheet that correspond to the pixels of the plots and insert theground truth data that model trainer 224 determines from the set ofmeasurements into the identified rows. Thus, model trainer 224 maycorrelate the set of measurements with the pixels of the image to createa labeled training data set that indicates the correct predictionsmachine learning model 222 should make based on the image data, climatedata, point cloud data, and/or other data about the geographical regiondepicted in the image.

Model trainer 224 may train machine learning model 222 based on theoutput of machine learning model 222 and the set of measurements. Forexample, model trainer 224 may input the spreadsheet with the labels forthe correct outputs, the image, and the other data into machine learningmodel 222. Model trainer 224 may execute machine learning model 222 andreceive predicted outputs of timber data. Model trainer 224 may comparethe predicted output (e.g., predicted timber data) with the expectedoutput (e.g., expected timber data) for the different pixels and use aloss function or another supervised training technique based on thedifferences between the two values for the individual pixels to trainmachine learning model 222. Model trainer 224 may use backpropagation todetermine a gradient for the respective loss function and update theweights and/or parameters of machine learning model 222 using thegradient, such as by using gradient descent techniques.

Data post-processor 226 may comprise programmable instructions that,upon execution, cause processor 212 to process the output timber datafrom machine learning model 222 to normalize the data for individualgeographic regions. For example, different geographic regions may haveset characteristics outside of the characteristics that are input intomachine learning model 222 to generate timber data. Examples of suchcharacteristics may be differences in species, climate, and soil type.To enable machine learning model 222 to be used for a diverse set ofgeographic areas with varying outside factors, and to reduce the numberof inputs into machine learning model 222, data post-processor 226 mayaccount for the different areas by using a normalization factor that isindividually associated with the respective area to normalize an outputfor timber data for an image depicting the area. Such normalizationfactors may be stored in normalization database 230 (e.g., a relationaldatabase that contains normalization factors for different types oftimber data for different geographic regions) in a look-up table thatmay be searched based on an input identifying the geographic area. Datapost-processor 226 may determine if the timber data needs to benormalized for an image of a geographic area by receiving an inputidentifying the geographic area and using the input as a look-up innormalization database 230.

If the data post-processor 226 identifies a normalization factor for thegeographic area depicted in an image, data post-processor 226 may adjustthe output timber data using the normalization factor. The normalizationfactor may be used as a multiplier or a divisor and may be specific todifferent types of timber data such as differences in species, climate,and soil type. Different geographic regions may have differentnormalization factors for any number of types of timber data. Datapost-processor 226 may retrieve the output timber data from machinelearning model 222 and apply the normalization factor to the outputtimber data to generate adjusted timber data for each pixel of an image.

Overlay generator 228 may comprise programmable instructions that, uponexecution, cause processor 212 to generate interactive overlayscomprising timber data for individual pixels of images based on theoutputs from machine learning model 222 and/or data post-processor 226.Overlay generator 228 may generate an interactive overlay from timberdata. Overlay generator 228 may do so by identifying the pixels thatcorrespond to predicted timber data and/or, in cases where adjustment isrequired, adjusted timber data. Overlay generator 228 may assign thetimber data to the corresponding pixels and generate an overlay withpixels that mirror the pixels of the image. Overlay generator 228 mayconfigure the different pixels of the overlay such that when a userplaces a cursor over a pixel or otherwise selects the pixel, the overlaywill display the timber data for the pixel. Overlay generator 228 mayplace or append the interactive overlay over the image so a user mayeasily view the timber data for the geographic regions that are depictedby the individual pixels.

100501 Referring now to FIG. 3 , a photograph 300 of an overhead view ofa wooded area is shown, in accordance with some embodiments. The woodedarea of photograph 300 may include a series of rivers and a variedlandscape of mountains and plains. As illustrated, a few locations onphotograph 300 are highlighted with circles. The circles may indicatelocations (e.g., plots) of the geographical region depicted in the imagein which one or more technicians visited and took sets of measurementsof the trees. The measurements from the different locations may be usedto train a machine learning model to predict timber data for individualpixels of images using photograph 300 and corresponding SAR imagery,other optical data, geospatial data, and a point cloud of a digitalsurface model of the geographical area depicted in photograph 300.

Referring now to FIG. 4 , a sequence diagram of a sequence 400 ofzooming in on a particular region of a photograph 402 is shown, inaccordance with some embodiments. Sequence 400 may be initiated when adata processing system (e.g., forest inventory manager 206) receives arequest from a client device asking for the volume of the timber withina highlighted portion of photograph 402. The data processing system mayreceive such a request after executing a machine learning model togenerate timber data for the pixels of photograph 402 or the dataprocessing system may execute the machine learning model in response toreceiving the request. The data processing system may identify thepixels in the highlighted portion and the timber data of the pixelswithin the highlighted portion. The identified pixels are represented aszoomed-in image 404. The data processing system may aggregate thevolumes that correspond to the pixels within the highlighted portion ofphotograph 402 or zoomed-in image 404 to generate a total volume for thehighlighted portion. The data processing system may then transmit thetotal volume to the requesting device in a record with any other timberdata that the device may have requested.

Referring now to FIGS. 5A and 5B, an illustration of light bands 502with which different satellite images may be collected and an image 500that is captured using one of the light bands is shown, in accordancewith some embodiments. The different light bands of light bands 500 mayeach represent different light spectrums that a satellite may use tocapture overhead images of different landscapes and wooded areas.Photographs taken using the different light bands may each havedifferent levels of detail and may, in combination with other data(e.g., geospatial data and digital surface model data), be used topredict timber data for different pixels of an image. For example, image502 may depict a wooded area in band 2 of light bands 500. Image 502 maybe concatenated into a feature vector with images of the same area indifferent light bands and/or a standard image, as well as geospatial andradar data, digital surface model data, and SAR imagery of thegeographical area to predict timber data for individual pixels of one ofthe images of the area that was used as an input. This combination ofdata may allow the machine learning model to accurately predict timberdata for the individual pixels of the image with a higher accuracy thanother systems that just typically rely on the image data itself topredict timber data. Particularly, the inclusion of the digital surfacemodel data in the input data may substantially improve the accuracy ofthe predicted timber data.

Referring now to FIG. 6 , a photograph 600 of an overhead view of awooded area annotated with plots from which tree measurements have beencollected is shown, in accordance with some embodiments. Photograph 600may be similar to photograph 300, shown and described with reference toFIG. 3 , but with less magnification in the lens that capturedphotograph 600. Using the systems and methods described herein, a dataprocessing system (e.g., forest inventory manager 206) may train amachine learning model to receive such images with other data about thedepicted region to predict timber data for the pixels of photograph 600.As illustrated, similar to photograph 300, photograph 600 includesmarkers 602 for locations at which technicians collected measurementdata of trees. Such data may be used to train a machine learning modelto predict timber data for images such as photograph 600 despite theimages showing a less nuanced view of a wooded area. Accordingly, anadvantage to using the systems and methods described herein is that themachine learning model may be trained to predict timber data for imagesirrespective of the resolution of the image taken by the satellite. Thesystems may do so as a result of the DSM data that provides a nuancedview of the depicted area in combination with geospatial and radar data.

Referring now to FIG. 7 , an example of a training data set 700 fortraining a machine learning model to generate timber data is shown, inaccordance with some embodiments. As illustrated, training data set 700may include columns for the different types of data that can be inputinto a machine learning model with an image to obtain timber data forindividual pixels of the image. Training data set 700 may include acolumn 702 of plot identification numbers for the different plots fromwhich data was collected. The plots may each correlate to a specific setof pixels of the image. Training data set 700 may also include a column704 of the volumes at the different plots. The volume may have beencalculated from the measurements the technicians captured at therespective plots. Column 704 for the volumes of the plots may operate asa label column indicating the correct predictions for the pixels thatcorrespond to the plots for which the volumes were determined. A dataprocessing system (e.g., forest inventory manager 206) may generate afeature vector of the optical data, SAR imagery data, digital surfacemodel data, and geospatial data of columns 706 a-706 g as well as anoverhead image of a wooded area and use the feature vector as an inputinto a machine learning model to obtain predicted volumes for thedifferent pixels of the image. The data processing system may comparethe predicted volume to the corresponding volume values of column 704,determine differences between the volumes, and train the machinelearning model based on the differences. Accordingly, the dataprocessing system may use training data set 700 to train the machinelearning model to predict volumes of different pixels of images. Themachine learning model may similarly be trained to predict one or moreother timber data types or individual machine learning models may beindividually trained to predict the different types of timber data.

Referring now to FIG. 8 , a graphical view of an example feature vector800 that can be used as input into a machine learning model to generatetimber data is shown, in accordance with some embodiments. Featurevector 800 may include optical data 802 (e.g., images captured usingdifferent light spectrums), SAR data 804 (e.g., radar data),topographical data 806 (e.g., elevation data), and other data 808 (e.g.,digital surface model data, climate data, soil data, etc.). Thedifferent types of data of feature vector 800 may each have values thatcorrespond to the same pixel of an image and/or a common geographicalregion depicted by the image. Accordingly, a data processing system(e.g., forest inventory manager 206) may use feature vector 800 topredict timber data for a geographical region more accurately (e.g.,15-20% more accurately) than systems that do not use the specificcombination of data of feature vector 800 to generate machine learningmodel predictions.

In some embodiments, example feature vector 800 may be a training dataset that can be used to train a machine learning model to predict timberdata for pixels of images. The data of the feature vector 800 may beaggregated from different sources and combined. For example, the datamay be processed so each data type has a matching resolution to eachother and corresponds to the same location or area of a geographicalregion depicted in an image. The data may also be reviewed by reviewersto remove any outliers that may be introduced as a result oftypographical errors or seemingly random weather patterns that do notaccurately reflect the area. The data may then be introduced as an inputinto the machine learning model for training to predict timber data forindividual pixels of images.

In some embodiments, during the training process, the parameters andweights of the machine learning model may be checked and adjusted toreduce any overtraining that may occur as a result of one training dataset. For instance, a reviewer or the data processing system may reviewthe weights of the machine learning model and any changes to the weightsthat may occur as a result of one training run and reduce the changethat resulted from the training run (e.g., reduce the change if thechange exceeds a threshold).

In some embodiments, the data processing system may train machinelearning models to predict timber data for images over time and selectthe models that make the most accurate predictions to use in practice.For example, after inputting a series of training data sets into themachine learning models for training, the data processing system mayevaluate the accuracy of the models by comparing the models' outputsagainst the expected values. The data processing system may select themachine learning model with the highest accuracy to use upon receiving arequest to generate timber data for a geographical area.

In some embodiments, the data processing system may train machinelearning models and select the models that require the least amount ofinput variables while still being accurate above a threshold. Forexample, the data processing system determine a machine learning modelmay have a 90% accuracy with image data, elevation data, DSM data, andsoil data, and an 80% accuracy with image data, climate data, and DSMdata. The data processing system may compare each accuracy to a definedaccuracy threshold of 75% and determine to use the machine learningmodel with the 80% accuracy because the model uses less inputs and stillhas an accuracy that exceeds the threshold.

In some embodiments, the data processing system may train machinelearning models to predict timber data for individual variables (e.g.,one machine learning model may predict timber volume, another machinelearning model may predict trees species data, another machine learningmodel predict maximum tree height, etc.). Accordingly, when the dataprocessing system receives a request for an overall forest inventory ofan area, the data processing system may input the same image and datafor the area in each machine learning model to obtain all of therequested timber data.

In summary, the data processing system may receive training data from avariety of data source providers as different types of data regardingdifferent geographical regions. The data processing system may intersectthe data to match the data that corresponds to the same geographicalarea or region. The data may then be reviewed for anomalous values andprocessed into a training data set. The training data set may be used totrain one or more machine learning models (e.g., a gradient boostingmachine learning model). The data processing system may tune theparameters of the machine learning model to avoid overtraining andperform a model selection process to identify the machine learningmodels that are accurate and require the least amount of inputs.

Referring now to FIG. 9 , an example method 900 for improved forestinventory management is shown, in accordance with some embodiments.Method 900 can be performed by a data processing system (a client deviceor a forest inventory manager 206, shown and described with reference toFIG. 2 , a server system, etc.). Method 900 may include more or feweroperations and the operations may be performed in any order. Performanceof method 900 may enable the data processing system to generate timberdata indicating characteristics about the volume, height, species mix,tree count, DBH parameters, and other characteristics about thevegetation that is depicted in an aerial image of a wooded area (e.g., aforest). The data processing system may collect data about such a woodedarea including SAR imagery, optical imagery (e.g., a photograph capturedby a satellite), geospatial data, and data from a digital surface modelof the wooded area. The data processing system may concatenate thecollected data into a feature vector. The data processing system maythen input the feature vector into a machine learning model and receivetimber data (e.g., species data and different types of trees mensurationdata) about the wooded data at the regions represented by the pixels ofthe image of the wooded area. The data processing system may thengenerate an interactive overlay with the timber data and overlay theinteractive overlay onto the image such that when a user places a cursorover or selects different pixels, the overlay may display timber dataabout the region represented by the selected pixel. The combination ofinputs into the machine learning model may enable the machine learningmodel to make predictions that are more accurate than other systems thatattempt to determine forest inventory data using aerial imagery.

At operation 902, the data processing system may receive an image of awooded area. The image may be an optical photograph of the wooded areataken from above the wooded area such as by a satellite or anotherflying vehicle. The data processing system may receive the image of thewooded area from an entity or company that specializes in capturing andtransmitting such photographs. For example, the data processing systemmay receive the image from an ESA Sentinel-2 satellite. Additionally, insome embodiments, the data processing system may receive photographs orradar data of the wooded area such as photographs or radar datacollected from ESA Sentinel-1 and/or ALOS-2 PALSAR satellites.

At operation 904, the data processing system may receive climate datafor a geographic region of the wooded area. The geographic region may bethe geographic area and/or coordinates of the wooded area (e.g., theclimate data for the coordinates of the geographic area). The climatedata may include information about the climate of the wooded area (e.g.,precipitation, temperature, solar radiation, etc.). The data processingsystem may receive the climate data from an online database or from adata source provider that collects and maintains records of the climatesaround the world (e.g., weather service providers, ClimNA, etc.). Insome embodiments, the data processing system may receive other datarelated to the wooded area such as the elevation and slope at differentpoints within the wooded area or of the wooded area as a whole. The dataprocessing system may receive such data from online data sourceproviders such as, but not limited to, the USGS National ElevationDataset. In some embodiments, the data processing system may collect orreceive soil data (e.g., the types of soil, the amount of soil, the PHlevel of the soil, etc.) about the wooded area. The data processingsystem may receive such soil data from data source providers such as,but not limited to, the gNATSGO database.

At operation 906, the data processing system may receive point clouddata of a digital surface model for the wooded area. The point clouddata may include the maximum height of the wooded area at variouspoints. For example, the point cloud data may indicate the maximumheight of the wooded area to be the highest points on trees, buildings,hills, etc., within the wooded area. The point cloud data may have a 25cm resolution (e.g., the point cloud may indicate the maximum heightevery 25 cm within the wooded area), or a resolution that is sharperthan the resolution of the climate data and/or optical or radar data.The data processing system may receive the point cloud data from a datasource provider that provides digital surface models for variousgeographic regions.

At operation 908, the data processing system may determine if the image,the climate data, and/or the point cloud of the digital surface modelhave matching resolutions. For example, because the different types ofdata are collected from different sources and generally collected usingdifferent methods, the data may be collected with differentgranularities and with different levels of detail. For example, theclimate data and/or soil data may be generic across the wooded areabecause there may not be much of a difference in climate or soil betweenthe areas represented by the pixels of the image. However, other data,such as point cloud data, elevation data, and/or slope data, may have ahigher resolution than the pixels of the image as it may be capturedusing a more nuanced device. The data processing system may compare theresolutions of the different types of data, including the image, thedata processing system has collected about the wooded area to determineif the data and the image have matching resolutions.

At operation 910, the data processing system may adjust the resolutionsof the data in response to determining the data does not have a matchingresolution. For example, the data processing system may determine thepoint cloud data of the digital surface model for the geographic regionof the wooded area has a resolution of 25 centimeters and the resolutionof the image is 10 meters. Accordingly, the data processing system mayreduce the resolution of the point cloud data to match the resolution ofthe image. To do so, the data processing system may identify the values(e.g., the height values) of the point cloud data that correspond toindividual pixels of the image (e.g., correspond to the same geographicarea as the individual pixels of the image). The data processing systemmay determine the average height values of the identified values foreach pixel of the image and generate a vector from the average heightvalues with a number of dimensions that match the number of pixels ofthe image (e.g., a vector with an average height value for each pixel ofthe image). Similarly, in another example, the data processing systemmay normalize the climate data, elevation data, slope data, and/or soildata into a vector that matches the number of pixels of the image. Sucha vector may include the same value at each index value of the imageunless more fine-grained data (e.g., higher resolution data) about thesoil, climate, or elevation is available for the geographic area that isdepicted in the image. This pre-processing technique may enable the dataprocessing system to evaluate the image and determine timber data forthe image on a pixel-by-pixel basis.

At operation 912, the data processing system may concatenate a featurevector from the received data. The data processing system may do so inresponse to determining, at operation 908, the collected data has amatching resolution to the image and/or after pre-processing thereceived data at operation 910. The data processing system mayconcatenate the values of the point cloud (e.g., the adjusted values ofthe point cloud) and the climate data to the image vector to create afeature vector that can be input into a machine learning model. In someembodiments, the data processing system may additionally or insteadconcatenate soil data, radar data, elevation data, etc., about thegeographic region with the image vector to create the feature vector.Accordingly, the data processing system may generate a feature vectorusing the image and information about the geographic region depicted inthe image that can be input into a machine learning model to generatetimber data about the vegetation of the wooded area depicted in theimage.

In some embodiments, the feature vector may be a spreadsheet or may begenerated from a spreadsheet. For example, the feature vector may be aspreadsheet with each row representing data for each pixel of the image.The columns of the spreadsheet may represent the different values fromthe different data sources. For instance, for one pixel, a row mayinclude the numerical value representing the pixel from the image andone or more of the average height of the point cloud data for the pixel,soil data for the pixel, elevation data for the pixel, radar data forthe pixel, slope data for the pixel, etc. Each row of the spreadsheetmay have similar data for the individual pixels. In such embodiments,concatenating the different types of data into a feature vector mayincluding adding the values for the data into the spreadsheet. The dataprocessing system may input the spreadsheet into the machine learningmodel as described herein.

In some embodiments, to input a spreadsheet into the machine learningmodel, the data processing system may retrieve the values for thedifferent types of data in the spreadsheet (e.g., values from thedifferent rows) and concatenate the values into a feature vector. Forexample, the data processing system may collect the data from differentsources and organize the data into different columns of a spreadsheet.The data processing system may execute a program that retrieves valuesfrom the different columns column-by-column and concatenates the valuesinto a single feature vector. Thus, the data processing system maygenerate a feature vector from a spreadsheet containing the differenttypes of data about the geographical data depicted in an image.

At operation 914, the data processing system may execute a machinelearning model (e.g., a support vector machine, a neural network, arandom forest, a regression algorithm such as a gradient boostingalgorithm, etc.). The machine learning model may be configured toreceive the feature vector that was generated at operation 912 anddetermine output timber data using learned parameters and/or weights topredict timber data based on the feature vector. The timber data mayinclude forest species and/or forest mensuration data for individualpixels of the image. For example, the data processing system may executethe machine learning model using the feature vector and the machinelearning model may output predictions of the distribution andintermixing of different tree and/or plant species at the geographiclocations that are depicted by different pixels of the image. Themachine learning model may instead or additionally output predictionsfor the total volume (e.g., amount of timber in the trees), tree count,height, and/or DBH parameters at the geographic locations that aredepicted by the pixels of the image.

At operation 916, the data processing system may determine if thefeature vector is being used to train the machine learning model. Thedata processing system may do so by determining if any labels correspondto the correct predictions for the timber data for individual pixels ofthe image. For example, the data processing system may parse aspreadsheet to determine if there is a column for “correct” values forwhat the machine learning model should have predicted based on the inputfeature vector. If the data processing system identifies such a column,the data processing system may determine the input feature vector is tobe used for training, otherwise, the data processing system maydetermine the input feature vector is not to be used for training. Insome embodiments, the data processing system may determine if thefeature vector is to be used for training based on whether theinstructions that the data processing system is processing includeinstructions to train the machine learning model according to labelsindicating the correct predictions for individual pixels (or sets ofpixels) of the image.

If the feature vector is being used to train the machine learning model,at operation 918, the data processing system may receive a set ofmeasurements for different areas of the wooded area depicted in theimage. The set of measurements may be “cruise data” that is generatedwhen technicians venture into the wooded area (e.g., the forest)depicted in the image and measure the vegetation (e.g., trees) in aseries of discrete locations (e.g., plots). A plot may be a circular orother shaped area and may be any size. The technicians may measure allor substantially all of the trees in the plots. In doing so, thetechnicians may take measurements such as the DBH, height, and/orspecies of the individual trees within the plots. The technicians maysubmit the measured data to the data processing system or anotherprocessing entity to send to the data processing system as ground truthdata about the vegetation of the respective plots. In some embodiments,the technicians may also count and transmit a total tree count of theplots.

In some embodiments, the data processing system may receive the measureddata and use a set of equations (e.g., allometric equations) todetermine the volume and other information about the vegetation of therespective plots. For instance, the data processing system may useallometric equations on the measured data to determine the volume of thetrees that were measured within the plots. The data processing systemmay also use the measured data to determine the average and/or maximumheight and/or DBH of the trees within the plots (e.g., determine theaverage height or DBH based on the measurements from the measured treesand/or identify the maximum height and/or DBH based on the highestmeasurements). Accordingly, from the set of measurements that thetechnicians measure and transmit to the data processing system, the dataprocessing system may determine the volume, average height, average DBH,maximum height, maximum DBH, species, and/or total tree count ofindividual plots within a geographic area as the ground truth data forthe plot.

At operation 920, the data processing system may correlate the set ofmeasurements with the pixels of the image. To do so, the data processingsystem may identify the pixel or set of pixels of the image thatcorrespond to the plots from which the set of measurements were taken.The data processing system may identify rows of the spreadsheet thatcorrespond to the pixels of the plots and insert the ground truth datathat the data processing system determines from the set of measurementsinto the identified rows. Thus, the data processing system may correlatethe set of measurements with the pixels of the image to create a labeledtraining data set that indicates the correct predictions the machinelearning model should make based on the image data, climate data, pointcloud data, and/or other data about the geographical region depicted inthe image.

At operation 922, the data processing system may train the machinelearning model based on the output of the machine learning model and theset of measurements. For example, the data processing system may inputthe spreadsheet with the labels for the correct outputs, the image, andthe other data into the machine learning model. The data processingsystem may execute the machine learning model and receive predictedoutputs of timber data. The data processing system may compare thepredicted output (e.g., predicted timber data) with the expected output(e.g., expected timber data) for the different pixels and use a lossfunction or another supervised training technique based on thedifferences between the two values for the individual pixels to trainthe machine learning model. The data processing system may usebackpropagation to determine a gradient for the respective loss functionand update the weights and/or parameters of the machine learning modelusing the gradient, such as by using gradient descent techniques.

If the data processing system determines that the feature vector is notbeing used for training at operation 916, at operation 924, the dataprocessing system may determine if the output timber data needs to benormalized based on the geographic region depicted in the image. Forexample, different geographic regions may have set characteristicsoutside of the characteristics that are input into the machine learningmodel to generate timber. Examples of such characteristics may be theair quality, proximity to human civilization, volcanoes in the area,proximity to the ocean, etc. To enable the same machine learning modelto be used for a diverse set of geographic areas with varying outsidefactors, and to reduce the number of inputs into the machine learningmodel, the data processing system may account for the different areas byusing a normalization factor that is individually associated with therespective area to normalize an output for timber data for an imagedepicting the area. Such normalization factors may be stored in adatabase within the data processing system in a look-up table that maybe searched based on an input identifying the geographic area. The dataprocessing system may determine if the timber data needs to benormalized for an image of a geographic area by receiving an inputidentifying the geographic area and using the input as a look-up in thedatabase.

If the data processing system identifies a normalization factor for thegeographic area depicted in the image, at operation 926, the dataprocessing system may adjust the output timber data using thenormalization factor. The normalization factor may be used as amultiplier or a divisor and may be specific to different types of timberdata. For example, if the geographic region is the salt flats in Utah,the normalization factor for images that depict the salt flats may be toreduce the tree volume by a factor of two and a tree count by a factorof four. Different geographic regions may have different normalizationfactors for any number of types of timber data. The data processingsystem may retrieve the output timber data from the machine learningmodel and apply the normalization factor to the output timber data togenerate adjusted timber data for each pixel of the image.

At operation 928, the data processing system may generate an interactiveoverlay from the timber data (e.g., adjusted timber data). The dataprocessing system may do so by identifying the pixels that correspond topredicted timber data and, in cases where adjustment is required,adjusted timber data. The data processing system may assign the timberdata to the corresponding pixels and generate an overlay with pixelsthat mirror the pixels of the image. The data processing system mayconfigure the different pixels of the overlay such that when a userplaces a cursor over a pixel or otherwise selects the pixel, the overlaywill display the timber data for the pixel. The data processing systemmay place the interactive overlay over the image so a user may easilyview the timber data for the geographic regions that are depicted by theindividual pixels.

In some embodiments, after generating timber data for the individualpixels of the image, the data processing system may be able to determinetimber data for various regions within the image based on the determinedtimber data for the individual pixels. For example, the data processingsystem may receive a request for the timber data in a specific area(e.g., volume, height, species, and/or total tree count of a particulararea depicted in the photograph). The data processing system mayidentify the pixels that depict the particular area and the timber datathat has been assigned to the individual pixels. Depending on therequest, the data processing system may aggregate or take the average ofthe timber data of all of the pixels that depict the area and generateaggregated timber data to provision (e.g., make available in a softwareas a service environment and/or transmit) to the requesting device. Forinstance, to provision the total volume of the area the data processingsystem may aggregate the volume for each pixel within the area. Toprovision the species, the data processing system may aggregate thedifferent species for each pixel. To provision the average height orDBH, the data processing system may determine an average height or DBHof all of the trees of the pixels in the area. To provision the maximumheight or DBH, the data processing system may identify the maximumheight or DBH of all of the pixels in the area. Thus, to determinetimber data for a particular geographical area, the data processingsystem may simply extract the values for the pixels that depict thegeographical area generating more accurate timber data for the areacompared with previous systems that often estimate data for the areabased on data from a portion of the area.

It is to be understood that any examples, values, graphs, tables, and/ordata used herein are simply for purposes of explanation and are notintended to be limiting in any way. Further, although the presentdisclosure has been discussed with respect to dam monitoring, in otherembodiments, the teachings of the present disclosure may be applied tosimilarly monitor other structures.

The herein described subject matter sometimes illustrates differentcomponents contained within, or connected with, different othercomponents. It is to be understood that such depicted architectures aremerely exemplary, and that in fact many other architectures can beimplemented which achieve the same functionality. In a conceptual sense,any arrangement of components to achieve the same functionality iseffectively “associated” such that the desired functionality isachieved. Hence, any two components herein combined to achieve aparticular functionality can be seen as “associated with” each othersuch that the desired functionality is achieved, irrespective ofarchitectures or intermedial components. Likewise, any two components soassociated can also be viewed as being “operably connected,” or“operably coupled,” to each other to achieve the desired functionality,and any two components capable of being so associated can also be viewedas being “operably couplable,” to each other to achieve the desiredfunctionality. Specific examples of operably couplable include but arenot limited to physically mateable and/or physically interactingcomponents and/or wirelessly interactable and/or wirelessly interactingcomponents and/or logically interacting and/or logically interactablecomponents.

With respect to the use of substantially any plural and/or singularterms herein, those having skill in the art can translate from theplural to the singular and/or from the singular to the plural as isappropriate to the context and/or application. The varioussingular/plural permutations may be expressly set forth herein for sakeof clarity.

It will be understood by those within the art that, in general, termsused herein, and especially in the appended claims (e.g., bodies of theappended claims) are generally intended as “open” terms (e.g., the term“including” should be interpreted as “including but not limited to,” theterm “having” should be interpreted as “having at least,” the term“includes” should be interpreted as “includes but is not limited to,”etc.). It will be further understood by those within the art that if aspecific number of an introduced claim recitation is intended, such anintent will be explicitly recited in the claim, and in the absence ofsuch recitation no such intent is present. For example, as an aid tounderstanding, the following appended claims may contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimrecitations. However, the use of such phrases should not be construed toimply that the introduction of a claim recitation by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim recitation to inventions containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should typically be interpreted to mean “atleast one” or “one or more”); the same holds true for the use ofdefinite articles used to introduce claim recitations. In addition, evenif a specific number of an introduced claim recitation is explicitlyrecited, those skilled in the art will recognize that such recitationshould typically be interpreted to mean at least the recited number(e.g., the bare recitation of “two recitations,” without othermodifiers, typically means at least two recitations, or two or morerecitations). Furthermore, in those instances where a conventionanalogous to “at least one of A, B, and C, etc.” is used, in generalsuch a construction is intended in the sense one having skill in the artwould understand the convention (e.g., “a system having at least one ofA, B, and C” would include but not be limited to systems that have Aalone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.). In those instances where aconvention analogous to “at least one of A, B, or C, etc.” is used, ingeneral such a construction is intended in the sense one having skill inthe art would understand the convention (e.g., “a system having at leastone of A, B, or C” would include but not be limited to systems that haveA alone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.). It will be furtherunderstood by those within the art that virtually any disjunctive wordand/or phrase presenting two or more alternative terms, whether in thedescription, claims, or drawings, should be understood to contemplatethe possibilities of including one of the terms, either of the terms, orboth terms. For example, the phrase “A or B” will be understood toinclude the possibilities of “A” or “B” or “A and B.” Further, unlessotherwise noted, the use of the words “approximate,” “about,” “around,”“substantially,” etc., mean plus or minus ten percent.

The foregoing description of illustrative embodiments has been presentedfor purposes of illustration and of description. It is not intended tobe exhaustive or limiting with respect to the precise form disclosed,and modifications and variations are possible in light of the aboveteachings or may be acquired from practice of the disclosed embodiments.It is intended that the scope of the invention be defined by the claimsappended hereto and their equivalents.

The foregoing outlines features of several embodiments so that thoseskilled in the art may better understand the aspects of the presentdisclosure. Those skilled in the art should appreciate that they mayreadily use the present disclosure as a basis for designing or modifyingother processes and structures for carrying out the same purposes and/orachieving the same advantages of the embodiments introduced herein.Those skilled in the art should also realize that such equivalentconstructions do not depart from the spirit and scope of the presentdisclosure, and that they may make various changes, substitutions, andalterations herein without departing from the spirit and scope of thepresent disclosure.

What is claimed is:
 1. A method, comprising: receiving, by one or moreprocessors, an image depicting an overhead view of a wooded area, theimage comprising a plurality of pixels; receiving, by the one or moreprocessors, a set of climate data for a geographic region in which thewooded area is located; receiving, by the one or more processors, apoint cloud of a digital surface model of the wooded area;concatenating, by the one or more processors, data corresponding to theplurality of pixels of the image, the set of climate data, and the pointcloud into a feature vector; executing, by the one or more processors, amachine learning model using the feature vector to generate timber datafor each of the plurality of pixels of the image; and generating, by theone or more processors, an interactive overlay from the timber data, theinteractive overlay comprising the generated timber data for each of theplurality of pixels of the image.
 2. The method of claim 1, furthercomprising: receiving, by the one or more processors, soil data andelevation data for the geographic region, wherein concatenating thedata, the set of climate data, and the point cloud into the featurevector further comprises concatenating, by the one or more processors,the soil data and the elevation data into the feature vector.
 3. Themethod of claim 1, further comprising: receiving, by the one or moreprocessors, a set of tree measurements for a plurality of regions withinthe wooded area, the set of tree measurements comprising measurementsfor individual trees of the plurality of regions; correlating, by theone or more processors, the set of tree measurements for the pluralityof regions with sets of pixels of the image; and training, by the one ormore processors, the machine learning model according to a loss functionbased on the generated timber data for the sets of pixels and the set ofmeasurements.
 4. The method of claim 3, wherein receiving the set ofmeasurements for the plurality of regions comprises receiving, by theone or more processors, tree species, tree diameter at breast height,tree volume, and tree count for trees within the plurality of regions.5. The method of claim 1, further comprising: retrieving, by the one ormore processors from a database, a geographic normalization factor basedon the geographic region of the wooded area; and adjusting, by the oneor more processors, the timber data based on the geographicnormalization factor, wherein generating the interactive overlay fromthe timber data comprises generating, by the one or more processors, theinteractive overlay from the adjusted timber data.
 6. The method ofclaim 1, wherein executing the machine learning model using the featurevector to generate the timber data comprises executing, by the one ormore processors, the machine learning model using the feature vector togenerate tree height, tree species, tree diameter at breast height, ortree count for trees within areas represented by the plurality ofpixels.
 7. The method of claim 1, wherein receiving the point cloud ofthe digital surface model of the wooded area comprises receiving, by theone or more processors, height values of a surface of the wooded area.8. The method of claim 7, further comprising: reducing, by the one ormore processors, a model resolution of the point cloud to match an imageresolution of the image, wherein concatenating the point cloud into thefeature vector comprises concatenating, by the one or more processors,the point cloud with the reduced model resolution into the featurevector.
 9. The method of claim 1, further comprising: configuring, bythe one or more processors, the interactive overlay to display timberdata for areas represented by individual pixels of the plurality ofpixels when a cursor overlays the individual pixels or upon a selectionof an individual pixel.
 10. The method of claim 1, further comprising:receiving, by the one or more processors from a client device, a requestfor regional timber data of a region of the wooded area; aggregating, bythe one or more processors, timber data associated with pixels of theplurality of pixels that depict the region of the wooded area; andprovisioning, by the one or more processor, the aggregated timber datato the client device.
 11. The method of claim 1, wherein concatenatingthe data corresponding to the plurality of pixels of the image, the setof climate data, and the point cloud into a feature vector comprisesadding, by the one or more processors, the data corresponding to theplurality of pixels of the image, the set of climate data, and the pointcloud into a spreadsheet.
 12. A system comprising: one or moreprocessors configured by machine-readable instructions to: receive animage depicting an overhead view of a wooded area, the image comprisinga plurality of pixels; receive a set of climate data for a geographicregion in which the wooded area is located; receive a point cloud of adigital surface model of the wooded area; concatenate data correspondingto the plurality of pixels of the image, the set of climate data, andthe point cloud into a feature vector; execute a machine learning modelusing the feature vector to generate timber data for each of theplurality of pixels of the image; and generate an interactive overlayfrom the timber data, the interactive overlay comprising the generatedtimber data for each of the plurality of pixels of the image.
 13. Thesystem of claim 12, wherein the one or more processors are furtherconfigured to: receive soil data and elevation data for the geographicregion, wherein the one or more processors are configured to concatenatethe data, the set of climate data, and the point cloud into the featurevector by concatenating the soil data and the elevation data into thefeature vector.
 14. The system of claim 12, wherein the one or moreprocessors are further configured to: receive a set of tree measurementsfor a plurality of regions within the wooded area, the set of treemeasurements comprising measurements for individual trees of theplurality of regions; correlate the set of tree measurements for theplurality of regions with sets of pixels of the image; and train themachine learning model according to a loss function based on thegenerated timber data for the sets of pixels and the set ofmeasurements.
 15. The system of claim 14, wherein the one or moreprocessors are configured to receive the set of measurements for theplurality of regions by receiving tree species, tree diameter at breastheight, tree volume, and tree count for trees within the plurality ofregions.
 16. The system of claim 12, wherein the one or more processorsare further configured to: retrieve, from a database, a geographicnormalization factor based on the geographic region of the wooded area;and adjust the timber data based on the geographic normalization factor,wherein the one or more processors are configured to generate theinteractive overlay from the timber data by generating the interactiveoverlay from the adjusted timber data.
 17. The system of claim 12,wherein the one or more processors are configured to execute the machinelearning model using the feature vector to generate the timber data byexecuting the machine learning model using the feature vector togenerate tree height, tree species, tree diameter at breast height, ortree count for trees within areas represented by the plurality ofpixels.
 18. A non-transitory computer-readable storage medium havinginstructions embodied thereon, the instructions being executable by oneor more processors to perform a method, the method comprising: receivingan image depicting an overhead view of a wooded area, the imagecomprising a plurality of pixels; receiving a set of climate data for ageographic region in which the wooded area is located; receiving a pointcloud of a digital surface model of the wooded area; concatenating datacorresponding to the plurality of pixels of the image, the set ofclimate data, and the point cloud into a feature vector; executing amachine learning model using the feature vector to generate timber datafor each of the plurality of pixels of the image; and generating aninteractive overlay from the timber data, the interactive overlaycomprising the generated timber data for each of the plurality of pixelsof the image.
 19. The non-transitory computer-readable storage medium ofclaim 18, wherein the method further comprises: receiving soil data andelevation data for the geographic region, wherein concatenating thedata, the set of climate data, and the point cloud into the featurevector further comprises concatenating the soil data and the elevationdata into the feature vector.
 20. The non-transitory computer-readablestorage medium of claim 18, wherein the method further comprises:receiving a set of tree measurements for a plurality of regions withinthe wooded area, the set of tree measurements comprising measurementsfor individual trees of the plurality of regions; correlating the set oftree measurements for the plurality of regions with sets of pixels ofthe image; and training the machine learning model according to a lossfunction based on the generated timber data for the sets of pixels andthe set of measurements.